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\title{TagLeak: Non-intrusive and Battery-free Liquid Leakage Detection with Backscattered Signals}

%\author{
%	\IEEEauthorblockN{Junchen Guo, Ting Wang, Long Liu, Songzhen Yang, Yuan He}
%	\IEEEauthorblockA{\textit{School of Software, Tsinghua University, P.R. China} \\
%		\{gjc16, wangting96, liulong16, yangsong96 \}@mails.tsinghua.edu.cn, yuanhe@mail.tsinghua.edu.cn
%	}
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\maketitle


\begin{abstract}

In modern factories, large-scale rotating machinery is often equipped with auxiliary machines for water cooling and lubricant looping. All the flanges along the pipelines and valves of those huge and complicated machines may face the problem of liquid leakage if they are not welded.
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Because it is very hard for traditional sensor-based detection approaches to cover all potential leakage points, nowadays this work is roughly solved by periodic manual checking.
%
In this paper, we propose TagLeak, a non-intrusive and battery-free liquid leakage detection approach with RFID backscattered signals.
%
TagLeak models the dynamic process of liquid leakage and inspects the continuous changes of amplitude and phase of tag readings.
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With elaborately-designed tag facilities and xxx-based detection algorithms, TagLeak can achieve the 90\% accuracy of leakage detection at the latency about 0.5 seconds, and can estimate the quantity of leakage within the error of 10ml.
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Moreover, as an exploration of industrial Internet, we have deployed a prototype of TagLeak in a real-world digital twin system Pavatar for liquid leakage detection in a ultra-high-voltage converter station.

\end{abstract}

\section{Introduction}

Liquid leakage is a common problem in today's factories with numerous pipelines and valves. Especially for large-scale rotating machinery, e.g. electric generators and synchronous compensators, auxiliary machines for water cooling and lubricant looping suffer from potential liquid leakage at every unwelded flanges due to loose screws and aged gaskets (Figure 1).
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Liquid leakage will not only affect the stability of these machines, but also trigger potential safety hazards, e.g. circuit shorts due to water leakage, or even
fire disasters due to lubricant leakage.
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Therefore, detection of liquid leakage is one of the most significant works to guarantee the safety of the daily operation and maintenance in modern industries.

Nevertheless, according to our preliminary consultations and discussions with the operators from no matter small thermal power plants or large converter stations, the works of liquid leakage detection are done manually at a median period of 2 hours. Either by visual inspections or by touching the surfaces of those machines, manual checking is not only time and labor consuming, but also inaccurate and difficult to be quantized and digitalized for further analysis.

Here, one might ask: why hasn't there been a universal and effective solution for automatic detection of liquid leakage in modern industries?
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With sufficient field studies, we conclude three main characteristics of liquid leakage as obstacles for conventional techniques as follows (Fig \ref{fig:background}).

\begin{itemize}
\item \textbf{Massive potential leakage points.} Traditional water-leakage sensors based on resistance changes often have limited detection range. However, massive potential leakage points along pipelines and valves are hard to be covered with these sensors at an acceptable cost.

\item \textbf{Frequent and random leakage time.} Liquid leakage occurs frequently and randomly. The renewal of intrusive and disposable water-leakage sensors is unaffordable for entrepreneurs as well as troublesome for factory workers.

\item \textbf{Rare and unobvious visual clues.} Due to deployment angles and visual obstacles, camera-based image or video analysis also suffer from the coverage problem. Besides, liquid leakage is often hard to spot because of painted surfaces and complicated light conditions.
\end{itemize}

\begin{figure}[tb]
	\centering
	\includegraphics[width=0.5\textwidth]{img/background.pdf}
	\caption{\textbf{Liquid leakage problems in modern factories.}}
	\label{fig:background}
\end{figure}

Could there be a detection approach that has a larger coverage, a lower cost and an robuster signal inputs?
%
Recent advances in battery-free backscattering with Radio Frequency IDentification (RFID) techniques might offer a solution.
%
Existing works have shown the potential for cross-modal sensing with just backscattered signals, e.g. vibration inspection \cite{TagBeat}, eccentricity detection \cite{RED}, humidity sensing\cite{Hum}, package verification \cite{Package}, activity recognition \cite{Shop} and touch detection \cite{RIO}.
%
All of these works demonstrate the RFID's capability of non-intrusive sensing, low cost and ease of deployment.
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A more relevant work TagScan \cite{TagScan} which leverages commercial off-the-shelf (COTS) RFID devices to classify more than 10 liquid materials has enlighten us on detecting liquid leakage with the backscattered signals.
%
However, during the preliminary exploration, we find liquid leakage detection with usable and robust RFID signals in real-world industries is non-trivial due to the following challenges:

\begin{itemize}
\item \textbf{Challenge 1}: Modeling the dynamic process of liquid leakage and making detection decisions with limited inputs, e.g. received signal strength (RSS) and phase is a challenging task, let alone evaluating the volume or the flow rate of the leaked liquid.

\item \textbf{Challenge 2}: Different from other RFID sensing systems, to detect the liquid leakage, RFID tags are deployed very close to liquid-rich areas. The absorption of RFID signals by the liquid cannot be ignored. However, existing works have not well described this phenomenon yet.

\item \textbf{Challenge 3}: In real-world deployment, interferences induced by the vibration of the monitored targets and the rich multipath due to complicated pipelines and unpredictable human activities make the signals vulnerable. Thus, how to make the detection practical and robust remains challenging.
\end{itemize}

In this paper, we propose TagLeak, a non-intrusive and battery-free liquid leakage detection approach with RFID backscattered signals.
%
TagLeak models the dynamic process of liquid leakage and inspects the continuous changes of amplitude and phase of tag readings.
%
\textbf{More high-level descriptions!!!}
%
The contributions of this paper are summarized into the following three aspects:

\begin{itemize}
	\item We design a special leakage detection tag (LDT) to capture the changes of backscattered signals induced by liquid leakage.
	
	\item We model the dynamic process of the impact of liquid leakage on RFID signals with the consideration of signal absorption by the liquid. And we introduce signal pre-processing algorithms for inference cancellation, and a Markov-chain based detection algorithm for robust liquid leakage detection.
	
	\item Moreover, we implement a prototype of TagLeak and evaluate it across various scenarios. It achieves 90\% accuracy of leakage detection at the latency about 0.5 seconds.
	%, and can estimate the quantity of leakage within the error of 10ml.
	
	\item Last but no least, as an exploration of industrial Internet, we have deployed the prototype of TagLeak in a real-world digital twin system Pavatar \cite{Pavatar} for liquid leakage detection in a ultra-high-voltage converter station.
\end{itemize}

The rest of this paper is organized as follows.
%
We discuss some preliminary intuition of TagLeak in Section II.
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The overview and design details of TagLeak are presented in Section III.
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Section IV describes the implementation and evaluation of TagLeak.
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Section V introduces related works of TagLeak.
%
Finally, we conclude TagLeak and discuss future works in Section VI.

\section{Related Works}
\subsection{Traditional leakage detection methods}

There are several pieces of prior work that attempt to detect the leakage of liquid in industrial environment.\cite{carrillo2002}propose a optical distributed sensor that use the swelling of the polymer and the concomitant compression of the optical fiber,which will increase the optical attenuation of a signal that travels through the fiber.\cite{ashauer1999thermal} presents a new type of a flow sensor which is based on a thermal principle.\cite{toth1997planar} developed a capacitive sensor with a high resolution, which uses a low-cost electrode structure.These traditional methods not only are extremely influenced by the surrounding environment,but also have the deployment problems in reality.

\begin{figure}[tb]
	\centering
	\includegraphics[width=0.5\textwidth]{img/propagation_3.pdf}
	\caption{\textbf{Experiment setup.}}
	\label{fig:preliminary}
\end{figure}

\subsection{RFID sensing applications}
The core idea of using RFID tags as sensors have been widely applied nowadays.The physical state of the tagged object as well as of the surrounding environment can be sensed by analyzing the change of tag's RSSI and phase. RIO \cite{RIO} uses the impedance of the antenna changes which manifests as a change in the phase of the RFID backscattered signal to detect touch and track.With the popularization of machine learning,some researches also use RFID system to classify and predict.IDSense\cite{Li2015IDSense} extracts the feature in the physical layer of the communication channel between the RFID tag and reader (such as RSSI, RF phase, and read rate) to classify the tagged object's motion events and touch events. GRfid\cite{Zou2017GRfid} have mined the spatial features of various gestures captured by COTS RFID devices to do recognition.

\subsection{Liquid sensing with RFID}
It's certain that liquid absorbs the electromagnetic waves,therefore,the communication between the RFID reader and tag will be affected.Gao discovered that changing antenna input impedance will result the change of minimum transmit power required to power-up the RFID tag\cite{Humidity-add}.
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\section{Preliminary}

In this section, we discuss some empirical studies of liquid leakage detection with COTS RFID devices, then try to explain the phenomena with some qualitative analyses, and finally present an opportunity for robust leakage detection in our solution TagLeak.

Figure \ref{fig:preliminary} shows the experiment setup for our preliminary studies. We attach an Alien Ultra-High-Frequency (UHF) passive RFID tag on a piece of absorbent cotton. An ImpinJ Speedway R420 RFID reader and a Laird circular polarized antenna are deployed to provide continuous waves and receive the backscattered signals from the tag between the cotton and antenna. When the liquid, e.g. pure water, drops from the unwelded and loose flange, the cotton absorbs it and makes a change to the readings of received signal strength (RSS) and phase.

% \subsection{Backscattered Signal Propagation Model}

% First of all, we qualitatively analyze the signal changes with a backscattered signal propagation model shown in Figure \ref{fig:preliminary}. In the ideal environment without multipaths, only two basic direct channels $A \rightarrow T_1$ and $T_1 \rightarrow A$ are considered and the liquid absorption won't affect RFID readings. However, there exist more propagation paths due to the multipath effect. We categorize all multipaths into the direct-reflection path $A \rightarrow M_1 \rightarrow T_1$ and through-cotton path $A \rightarrow M_2 \rightarrow C \rightarrow T_1$.

\begin{figure}[tb]
	\centering
	\includegraphics[width=0.5\textwidth]{img/absorption.eps}
	\caption{\textbf{Absorption Effect of Liquid to RFID Signals.}}
	\label{fig:absorption}
\end{figure}


\subsection{Absorption Effect of Liquid}

Figure \ref{fig:absorption} shows the changes of backscattered signals along with the liquid absorption in the cotton. We add 2mL water to the cotton at each time and sample the readings when the signals are stable to exclude the effect of liquid diffusion.
%

With the stable environment, we observe that the RSSI first surprisingly rises a little and then declines until the backscattered signal is unreadable by the COTS reader, while the phase steadily drops with the increase in liquid volume. 

\begin{figure*}[tb]
	\centering
	\subfigure[Interference Example]{\includegraphics[width=0.24\textwidth]{img/absorption.eps}}
	\subfigure[Detection with paired tags]{\includegraphics[width=0.24\textwidth]{img/0420-3.png}}
	\subfigure[Detection with separate tags]{\includegraphics[width=0.24\textwidth]{img/0420-4.png}}
	\subfigure[????]{\includegraphics[width=0.24\textwidth]{img/absorption.eps}}
	
	\caption{\textbf{Experiment results.}}
	\label{fig:experiments}
\end{figure*}

Since, the cotton which absorbs the liquid does not block the line-of-sight (LOS) propagation, we call this phenomenon \textbf{absorption effect}.
%
TagScan \cite{TagScan} does not consider this effect because the liquid container is very close to the antenna while the tag is hanging in the air and a little far from them. Thus, it just describes the attenuation of the LOS propagation.
%

A series of work leveraging RFID techniques for humidity sensing has discussed this effect \cite{Humidity-original}\cite{Humidity-print}\cite{Humidity-add}.
%
According to \cite{RFID-Environment}, as water drops increase the relative permittivity of the absorbent cotton, the electric field near the object's surface will decrease due to a higher permittivity and then the performance of tag antenna will degrade due to the ohmic loss and the change in its antenna impedance. According to the Friis transmission equation of RFID backscattering, we get the receiving power of tag $T$'s chip with:

\begin{equation}\label{f1}
	P_{R \rightarrow T_c} = P_{R \rightarrow T} \cdot \tau = (\frac{\lambda}{4\pi d})^2 P_{R} G_R G_T \eta \cdot \tau
\end{equation}

where $P_{R}$ is the transmission power of the reader $R$, $G_A$ and $G_T$ are the gain of $R$'s antenna and $T$'s antenna correspondingly. $\eta$ is the polarization mismatch factor, $\lambda$ is the wave length and $d$ is the distance between $R$'s antenna and $T$. The power transfer coefficient $\tau$ is defined as:

\begin{equation}\label{f2}
	\tau = 1 - |\rho|^2 = 1 - |\frac{Z_c - Z_a^*}{Z_c + Z_a}|^2, 0 \leq \tau \leq 1
\end{equation}

% where $\rho = \frac{Z_c - Z_a^*}{Z_c + Z_a}$ is the power reflection coefficient of $T$, $Z_c$ and $Z_a$ is the chip impedance and antenna impedance of $T$, and $Z_a^*$ is the complex conjugate of $Z_a$.

where $\rho = \frac{Z_c - Z_a^*}{Z_c + Z_a}$ is the power reflection coefficient of $T$, $Z_c$ and $Z_a$ is the chip impedance and antenna impedance of $T$, and $Z_a^* = R_a - j X_a$ is the complex conjugate of $Z_a = R_a + j X_a$.

Similarly, in the reverse link, the received power of the backscattered signal at the reader's antenna is calculated as:

\begin{equation}\label{f3}
P_{T \rightarrow R} = P_{R \rightarrow T} \cdot K \cdot (\frac{\lambda}{4\pi d})^2 G_R G_T \eta = (\frac{\lambda}{4\pi d})^4 P_{R} G_R^2 G_T^2 \eta^2 \cdot K
\end{equation}

where $K = |1 - \rho|^2$ is the impedance mismatch factor which describes the power reflection in the reverse link. To modulate a bit sequence, the tag's chip controls the value of its chip impedance $Z_c$ to manipulate the value of $K$ which further changes the received power. Suppose $Z_{c0}$ and $Z_{c1}$ are the controlled chip impedances for symbol $0$ and symbol $1$ correspondingly, and $\rho_0$ and $\rho_1$ are the reflection coefficients. By removing the direct-current components of the voltages induced by these two modulated signals, the received power at the reader can be represented as (details can be found in \cite{RFID-principle}):

\begin{equation}\label{f3}
P_{rcv} = (\frac{\lambda}{4\pi d})^4 P_{R} G_R^2 G_T^2 \eta^2 \cdot \frac{1}{4} |\rho_0 - \rho_1|^2
\end{equation}

%Normally, the tag $T$ switches its chip impedance $Z_c$ between $Z_a^*$ (impedance matching state, $K^{<match>} = 1$) and 0 (short state, $K^{<short>} = |\frac{Z_a + Z_a^*}{Z_a}|^2 = \frac{4R_a^2}{R_a^2 + X_a^2}$) to modulate backscattered signals. Normally, $R_a$ and $X_a$ are only determined by the design of the tag's antenna. 

%Nevertheless, as we have mentioned above, their value will change if the tag is close to some dielectric. Since RSSI is in proportion to the $P_{T \rightarrow R}^{<match>} / P_{T \rightarrow R}^{<short>}$, it varies when $Z_a$ changes. 

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%55

% The tag's chip controls the value of $Z_c$ for the power charge and the signal modulation. When the antenna impedance is perfectly matched, that is $Z_c = Z_a^*$, the power transfer coefficient $\tau = 1$, which means there is no power reflections and all incoming power is transfered to tag's chip. Nevertheless, as we mentioned before, when the tag is close to some dielectric, its antenna impedance $Z_a$ will change to some value $\overline{Z_a}$. Thus, the impedance mismatch ($Z_c = Z_a^* \neq \overline{Z_a}^*$ and $\tau < 1$) occurs, and the ratio of receiving power to transmission power at the reader side decreases. 

Suppose the transmission power, the environment and the antenna gains are kept unchanged, the RSSI readings are in direct proportion to the modulation factor $\Delta \rho = \frac{1}{4} |\rho_0 - \rho_1|^2$.
%
As we mentioned before, when the tag is close to some dielectric, its antenna impedance $Z_a$ changes to some value $\overline{Z_a}$. Thus, the received power $P_{rcv}$ and RSSI readings change.

However, due to the hardware imperfection, the practical antenna impedance does not equal to the ideal value, so the tag is initially not perfectly matched and the system suffers from the mismatch loss. 
%
Therefore, the RSSI tendency in Figure \ref{fig:absorption} can be explained as the change of $\overline{Z_a}$ first compensates for the imperfect mismatch and then deviates the system from the perfect match. Finally, when it makes $P_{R \rightarrow T_c}$ smaller than the activation power of $T$'s chip, the tag is not activated for backscatter communication.

\subsection{Handling Interferences with Tag Array}

We conduct the above measurement in an ideal environment with negligible dynamic multipaths. However, real-world signal measurement suffers from multiple sources of  interferences, especially that caused by the machine vibration, because the piece of absorbent cotton with the RFID tag attached to a flange will vibrate along with the rotating machine. Although the vibration is very weak, but according to \cite{TagBeat} it significantly affects the readings. Moreover, the readings are also susceptible to other dynamic multipath, e.g. human activity.

To deal with these interferences, we attach another RFID tag on the reverse side of the cotton. We denote the original tag as $T_1$ and the second one as $T_2$, so the setup can be represented as Antenna-$T_1$-Cotton-$T_2$. This design has the following advantages:

\begin{itemize}
	\item The adjacent tags are expected to face similar interferences in various environment, which can be leveraged for interference localization and cancellation.
	\item The tag-stacked design saves space in real-world deployment.
	\item Since the cotton is at the non-line-of-sight (NLOS) path of $T_2$, its readings are expected to present different features from those of $T_1$.
\end{itemize}

Figure \ref{fig:experiments}(a) shows the interferences caused by the machine vibration and human activity. Those signal fluctuations are similar in the reading of the arrayed tags, which can be leveraged to locate interferences and further cancel them. We discuss the details of our basic signal pre-processing algorithms for TagLeak in Section \ref{sec:design:preprocessing}.

\subsection{Coupling Effect of Adjacent Tags}

Although the paired tag makes the system more robust in practical, it incurs another obscure phenomenon. As shown in Figure \ref{fig:experiments}(b), after $T_2$ is attached, the RSS readings of it are too weak (around -70 dBm) to be stably detected by COTS RFID readers. Besides, when the cotton absorbs a certain amount of water, the RSS readings of $T_2$ surprisingly increase and surpass those of $T_1$, which are deemed to be larger since $T_1$ is closer to the antenna and it does not suffer from the LOS block.

First, we reckon the multipath effect to be the cause of unexpected signal abnormals, but then deny this hypothesis after conducting some experiments for reliability test (Figure 5). 
%
Since this phenomenon occurs only after we bring in the paired tag $T_2$, it might result from the interaction between these two closely-deployed tags. We repeat the above experiment by separately deploying $T_1$ and $T_2$ while keeping other conditions unchanged. 
%
The differences between Figure \ref{fig:experiments}(b) and \ref{fig:experiments}(c) prove the latter hypothesis. 

The interaction between adjacent RFID tags is called \textbf{coupling effect}. The backscattered waves from one tag $T_1$ generate an induced voltage in the antenna of one nearby tag $T_2$, which is equivalent to place a mutual impedance on $T_2$'s antenna \cite{Twins}. Consequently, when $T_2$ modulates signals, the impedance mismatch occurs again. A recent work Twins \cite{Twins} introduces a tag-structure-aware mutual effect model and leverages it for a better tracking performance. However due to the differences in propagation paths, the original induced currents in $T_1$ and $T_2$ are initially different and so are the mutual impedances.

\subsection{An Opportunity of TagLeak and Its Reliability}

By empirical measurements with different antenna-tag distances in different multipath environment (Figure 5), we exclude the suspicion of multipath effects and prove the stability of this unexpected phenomenon.

\section{Design}
\subsection{Overview}
\subsection{Tag Design}
\subsection{Dynamic Model of Liquid Leakage}
\subsection{Signal Pre-Processing}
\label{sec:design:preprocessing}

\subsection{Leakage Detection Algorithm}

\section{Evaluation}
\subsection{Implementation}
\subsection{Methodology}

\section{Conclusion}

In this work, we present TagLeak, a non-intrusive liquid leakage detection system with RFID backscattered signals.

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